Forecasting annual geophysical time series

Abstract

An important test of the adequecy of a stochastic model is its ability to forecast accurately. In hydrology as in many other disciplines, the performance of the model in producing one step ahead forecasts is of particular interest. The ability of several stationary nonseasonal time series models to produce accurate forecasts is examined in this paper. Statistical tests are employed to determine if the forecasts generated by a particular model are better than the forecasts produced by an alternative procedure. The results of the study indicate that for the data sets examined, there is no significant difference in forecast performance between the nonseasonal autoregressive moving average model and a nonparametric regression model.